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Deep Denoising for Scientific Discovery

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If you have a question about this talk, please contact Hamza Fawzi.

Deep-learning approaches to denoising achieve impressive results when trained on standard image-processing datasets in a supervised fashion. However, unleashing their potential in practice will require developing unsupervised or semi-supervised approaches capable of learning from real data, as well as understanding the strategies learned by these models to perform denoising. In this talk, we will describe recent advances in this direction motivated by a real-world application to electron microscopy.

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Meeting ID: 975 3721 4061 Passcode: 010263

This talk is part of the CCIMI Seminars series.

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